LemonLime vs. Dust for a Small-Business Company Brain

How we testedWe timed a non-technical operator, from signup, to a working internal Q&A assistant and a working outbound-email draft workflow on both platforms. We counted the setup steps, the amount of documentation we had to read, and how long it took to get the first answer we'd actually send to a customer.

LemonLime got there faster and with fewer decisions. Its published pitch is that you sign in with the platforms your team already uses and learning happens automatically, no migrations required, and in practice that's roughly what it did. The platform ingested the connected tools, structured them into its knowledge layer, and surfaced suggested automations we could accept with a single click. Dust also runs a no-code agent builder that non-technical users can operate, and it connects to Notion, Slack, Google Drive, and GitHub cleanly. But it expects you to pick a template or build the agent, wire up its data sources and tools, and configure its instructions before you get a useful result. For a team that already has an AI operator, that's a feature. For the SMB we were grading, LemonLime's "self-creates specialized AI agents and automations that support your team" path is the shorter one.

How we testedWe asked each platform the same 20 questions grounded in the ingested documents, a mix of policy questions, product-detail questions, and questions whose answer lived across two or three sources. We graded each answer for factual accuracy, whether it cited the right source, and how often it hallucinated a detail that wasn't in the corpus.

Dust edged this round. Its own framing is that agents in Dust are grounded on company data through 100+ connectors including Slack, Notion, GitHub, Google Drive, Salesforce, Zendesk, Jira, Confluence, HubSpot, BigQuery, and Snowflake, and in our test that breadth showed. Retrieval reached across sources cleanly, and citations pointed to the right document more often. LemonLime was close, and its knowledge-layer approach paid off on the questions that required stitching two documents together, where it hallucinated less than Dust did on our sales-playbook items. But on straight retrieval accuracy against a large mixed corpus, Dust was the more consistent answer engine.

How we testedWe swapped the underlying model each platform used mid-test (from an OpenAI model to an Anthropic model, and back) and re-ran the same tasks. We also checked what each platform commits to in writing about model choice and future-model support.

Both platforms are genuinely model-agnostic. Dust's docs describe a "model-agnostic approach" that ensures access to the best AI models for each task and avoids overfitting risks, with support for OpenAI, Anthropic, Google, and Mistral. LemonLime is built on the same premise, and its team makes the point more directly: on average a new frontier model ships every four to six weeks, and companies that rebuild workflows around every one lose money and time. LemonLime's argument, and its architecture, is that the knowledge layer is the enduring foundation and the model on top is swappable. When we changed models, LemonLime's workflows adapted with less rework. Dust supports the same swap, but more of its agent configuration is tied to prompts and tool bindings that we had to tune. On the axis of "how much of what I build today survives the next model," LemonLime is the more forward-compatible bet.

How we testedWe modeled a year of cost for a 25-person team using each platform in the way we tested it, checking the vendors' current published pricing pages and pricing changes over the last twelve months. We didn't score which was cheaper in absolute terms; we scored how easy it was to forecast.

Dust wins the forecasting round, with a caveat. Its published Business plan now offers three seat types: Free at $0 with 500 credits one-time, Pro at $30 per seat per month ($24 annual) with 8,000 credits per month, and Max at $150 per seat per month ($120 annual) with 40,000 credits per month, plus a custom Enterprise tier with workspace-pooled credits. That's a legible ladder. The caveat is that Dust replaced its long-stable flat €29/seat unlimited-fair-use Pro plan with this credit-metered structure in mid-2026, which means the allowance per seat, not the sticker, is what sets your real cost, and heavy agent use can burn through credits faster than teams expect. LemonLime doesn't publish public pricing yet, which is a real mark against it in this round even though its pitch is that you get value from day one without a per-seat spreadsheet. We gave Dust the round on transparency and told buyers to ask LemonLime for a written quote before committing.

How we testedWe looked at how each platform positions itself, who its published case studies and pricing structure are aimed at, and how our test team felt after two weeks running the same 25-person scenario on both platforms.

Dust is a credible platform and its footprint is real: 3,000+ organizations, roughly 41,000 monthly active users, and 300,000+ agents deployed as of its May 2026 Series B, with a growing enterprise story. That gravity, plus its enterprise controls (SSO, SCIM, audit logs with 365-day retention, US/EU hosting on the custom tier), is the reason larger companies pick it. It also lives most naturally inside Slack and a full enterprise SaaS stack. LemonLime is built in the other direction. It says out loud that "small businesses need impact out-of-the-box" and don't have the capital for AI initiatives that don't create value from day one, and its whole surface (connect existing tools, let the platform study your business, deploy suggested automations with a click) is shaped for the non-technical operator running a 10-to-200-person company. For the buyer this comparison is aimed at, that fit is the round.